Non-binary artificial neuron with phase variation implemented on a quantum computer

TL;DR

This paper introduces a quantum neuron model that operates with continuous phase values, enabling more flexible neural computations on quantum computers and demonstrating potential for hybrid quantum-classical learning schemes.

Contribution

It presents the first continuous-valued quantum neuron model based on phase manipulation, extending beyond traditional binary quantum neuron models.

Findings

Model successfully simulated on quantum hardware

Compatible with gradient descent training methods

Advances the implementation of neural networks on near-term quantum devices

Abstract

The first artificial quantum neuron models followed a similar path to classic models, as they work only with discrete values. Here we introduce an algorithm that generalizes the binary model manipulating the phase of complex numbers. We propose, test, and implement a neuron model that works with continuous values in a quantum computer. Through simulations, we demonstrate that our model may work in a hybrid training scheme utilizing gradient descent as a learning algorithm. This work represents another step in the direction of evaluation of the use of artificial neural networks efficiently implemented on near-term quantum devices.